Inversion of Physiological Information of Lettuce Polluted by Particulate Matter Based on Optimal Spectral Characteristic Variables
KONG Li-juan1,SUI Yuan-yuan2,LIU Shuang3,CHEN Li-mei1, ZHOU Li-na1, LIU Chun-hui1, JIANG Ling1, LI Song1, YU Hai-ye2*
1. College of Engineering and Technology, Jilin Agricultural University, Changchun 130022, China
2. College of Biological and Agricultural Engineering, Jilin University, Changchun 130000, China
3. College of Horticulture, Jilin Agricultural University, Changchun 130022, China
Abstract：The quality and yield of leafy vegetables are closely related to the net photosynthetic rate. Affected by particulate matter pollutionespecially in autumn and winter, the photosynthesis of leafy vegetables in greenhouses is restricted, which is unfavorable for accurate prediction of physiological information. Under the growth environment of particle pollution, The lettuce during the harvest period was the test object. The optimal method for establishing the inversion model of lettuce net photosynthetic rate based on hyperspectral technology was studied. The net photosynthetic rate and hyperspectral data of lettuce were Obtained. The Ratio Vegetation Index (RI), Difference Vegetation Index (DI), Normalized Difference Vegetation Index (NDVI), visualization Visualize Atmospherically Resistive Vegetation Index (VARI), Renormalized Difference Vegetation Index (RDVI), Perpendicular Vegetation Index (PVI) and Vegetation Attenuation Index (PSRI) , were selected. The original and first-order transformations of the 14 spectral vegetation indices were studied. The correlation matrix method was used to optimize the optimal vegetation index, which was related to the spectral position variable (Red edge magnitude, Dr) and spectral area variables, namely red edge area (SDr), the ratio of red edge area to blue edge area (SDr/SDb), the ratio of red edge area to yellow edge area (SDr/SDy) as spectral feature variables. The inversion model of the net photosynthetic rate about lettuce was established by using a combination of polynomial fitting and multivariate scatter correction (MSC), standard normal variable transformation (SNV), partial least squares (PLS) and principal component regression (PCR) . The results showed that PSRI (515, 499) and DI (515, 499) at the optimal wavelength position had the greatest correlation with net photosynthetic rate, which could reflect more physiological information of lettuce under particulate pollution. The combined modeling method of SG+MSC+PCR had the highest accuracy, the coefficient of determination Rc was 0.901 1, and the Rp was 0.945 8. The modeling effect based on the optimal spectral vegetation index was the best. The spectral area variable SDr/SDb had the highest fitting accuracy (R2=0.936 5), which could reliably predict the net photosynthetic rate of lettuce. It was the optimal method to establish the net photosynthetic rate of lettuce based on the spectral position and area variables. This work could provide a certain reference value for the inversion of plant physiological information using hyperspectral technology under a particulate pollution environment.
孔丽娟，隋媛媛，刘 爽，陈丽梅，周丽娜，刘春慧，姜 玲，李 松，于海业. 最优光谱特征变量反演颗粒物污染生菜的生理信息[J]. 光谱学与光谱分析, 2024, 44(04): 1128-1135.
KONG Li-juan,SUI Yuan-yuan,LIU Shuang,CHEN Li-mei, ZHOU Li-na, LIU Chun-hui, JIANG Ling, LI Song, YU Hai-ye. Inversion of Physiological Information of Lettuce Polluted by Particulate Matter Based on Optimal Spectral Characteristic Variables. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(04): 1128-1135.
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